A vignette study of mental health literacy for binge-eating disorder in a self-selected community sample
Bibliographic record
Abstract
Abstract Background Mental health literacy has implications for mental disorder recognition, help-seeking, and stigma reduction. Research on binge-eating disorder mental health literacy (BED MHL) is limited. To address this gap, our study examined BED MHL in a community sample. Method Two hundred and thirty-five participants completed an online survey. Participants read a vignette depicting a female character with BED then completed a questionnaire to assess five components of BED MHL (problem recognition, perceived causes, beliefs about treatment, expected helpfulness of interventions, and expected prognosis). Results About half of participants correctly identified BED as the character’s main problem (58.7%). The most frequently selected cause of the problem was psychological factors (46.8%) and a majority indicated that the character should seek professional help (91.9%). When provided a list of possible interventions, participants endorsed psychologist the most (77.9%). Conclusions Compared to previous studies, our findings suggest that current BED MHL among members of the public is better, but further improvements are needed. Initiatives to increase knowledge and awareness about the symptoms, causes, and treatments for BED may improve symptom recognition, help-seeking, and reduce stigma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".